Bibliographic record
Abstract
The purpose of this workshop is to present alternative strategies of instruction that will make the subjects of mathematics and statistics more accessible to students with non-mathematics backgrounds. It is not surprising that introductory mathematics and statistics courses can seem a little overwhelming and inaccessible to students with non-mathematics backgrounds. As a result, these students tend to feel distanced from the course material, or even discouraged from approaching instructors or teaching assistants (TAs) for help. The audience for this workshop includes graduate student TAs, post-doctoral fellows, instructors, lecturers, and anyone who wants to make mathematics and statistics a more engaging subject for students without the technical background. The focus of this workshop will be two-fold. First, we will examine how mathematics/statistics instructors can explain concepts to students of different backgrounds effectively via various role-play scenarios. Second, we will use the jigsaw technique to break up complex mathematical problems into pieces with the aim of encouraging collaboration and student engagement (Perkins & Saris, 2001). By attending this workshop, instructors will be able to help undergraduate students see mathematics as a more enjoyable learning experience that they can apply in their own respective fields. These two activities will help students with non-mathematical backgrounds feel more engaged with the material and become more confident when asking for help from an instructor or TA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".